Crohn's disease (CD) and ulcerative colitis (UC) are chronic immune-mediate diseases that affect an estimated 1.5 million Americans and account for 1.5 million in direct healthcare costs. Due to their young age of onset and protracted course characterized by relapses, hospitalizations, and surgery, they exert a considerable toll on patients and society. Therapeutic advances with the availability of monoclonal antibodies against tumor necrosis factor ? (anti- TNF) and anti-integrin (vedolizumab) therapies have made achievement of durable clinical and endoscopic remission increasingly possible. However, one-fifth of patients fail to achieve even an initial response and a similar proportion response annually. At present, choice of therapy is largely non-selective with sequential trial of agents from different therapeutic classes without a priori prediction of likelihood of response. However, this results in protracted morbidity due to inadequate treatment of disease and increases likelihood of permanent bowel damage in addition to exposing patients to ineffective but potentially toxic agents. Thus, there is an important unmet need for tools to predict response to each individual therapeutic class. Clinical factors such as smoking, duration of disease, location and behavior of involvement, have proved inconsistent in predicting response to therapy. In our previous work, we developed a prediction tool using genetic risk alleles to identify primary and secondary non-responders to anti-TNF therapy. This tool demonstrated both the utility of genetics and superiority of it as a predictor compared to clinical data. However, limitations to that work include retrospective adjudication of treatment response categories and reliance on improvement of symptoms which correlate poorly with endoscopic severity of inflammation, arguably a more robust marker. In this proposal, we aim to validate our prediction tool in an independent prospective cohort of patients initiating anti-TNF therapy, and important to assess its utility in determining response based on change and normalization of fecal calprotectin, a sensitive objective marker of intestinal inflammation. In addition, with the goal of developing tools to aid in personalized precise therapy by identifying mechanisms of action most likely to be of benefit to each patient, we will examine the predictive value of our existing therapy response tool in a prospective cohort of patients on treatment with vedolizumab. If our existing model is unable to predict response to vedolizumab, we will develop a separate predictive model with distinct polymorphisms to predict response to this therapeutic class. Importantly, the insights from this proposal will serve as a foundation for development of a comprehensive predictive model that also incorporates composition and metagenomic function of the gut microbiome that will significantly further our aim of providing personalized, precise care to our patients and ensuring optimal outcomes. This grant will also provide important preliminary data in support of the applicants future independent R01 funding proposal, and is a critically important step in the long-term academic success of the applicant.